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How Reliable Are Predicted MOS for Reproducing Human System-Level Preferences in Speech Enhancement?
Nahomi Kusunoki, Tsubasa Ochiai, Naohiro Tawara +4
We investigate whether predicted Mean Opinion Scores (MOS) can reliably support system-level comparisons of speech enhancement (SE) methods by introducing system-level preference a…
Dissecting the Segmentation Model of End-to-End Diarization with Vector Clustering
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +4
End-to-End Neural Diarization with Vector Clustering is a powerful and practical approach to perform Speaker Diarization. Multiple enhancements have been proposed for the segmentat…
Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada +10
Spatial Semantic Segmentation of Sound Scenes (S5) aims to enhance technologies for sound event detection and separation from multi-channel input signals that mix multiple sound ev…
Mamba-based Segmentation Model for Speaker Diarization
Alexis Plaquet, Naohiro Tawara, Marc Delcroix +3
Mamba is a newly proposed architecture which behaves like a recurrent neural network (RNN) with attention-like capabilities. These properties are promising for speaker diarization,…
SoundBeam meets M2D: Target Sound Extraction with Audio Foundation Model
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +4
Target sound extraction (TSE) consists of isolating a desired sound from a mixture of arbitrary sounds using clues to identify it. A TSE system requires solving two problems at onc…
Interaural time difference loss for binaural target sound extraction
Carlos Hernandez-Olivan, Marc Delcroix, Tsubasa Ochiai +3
Binaural target sound extraction (TSE) aims to extract a desired sound from a binaural mixture of arbitrary sounds while preserving the spatial cues of the desired sound. Indeed, f…